Outline

Ingegneria Sismica

Ingegneria Sismica

Driving Digital Shift: Carbon Emissions Trading as a Catalyst for Corporate Transformation in China–A Dual Machine Learning and DID Approach

Author(s): Haizhou Wang1, Pengfei Zuo2
1School of Business, University of Chinese Academy of Social Sciences, Beijing, 102488, China
2Institute of Quantitative & Technological Economics, Chinese Academy of Social Sciences, Beijing, 100732, China
Wang, Haizhou . and Zuo, Pengfei . “Driving Digital Shift: Carbon Emissions Trading as a Catalyst for Corporate Transformation in China–A Dual Machine Learning and DID Approach.” Ingegneria Sismica Volume 43 Issue 3: 1-17, doi:10.65102/is20261097.

Abstract

In response to the insufficient research on the collaborative mechanism between environmental regulation and enterprise digital transformation, this article proposes three hypotheses based on Porter’s theory of competitive advantage: direct driving of carbon trading policies, alleviation of financing constraints, and green technology innovation; Then, using panel data from Chinese A-share listed companies from 2010 to 2021, and combining dual machine learning and double difference method, a quasi-experimental study was conducted to systematically identify the net effect of policies. The empirical verification results show that the carbon trading pilot policy significantly promotes the digital transformation of enterprises, and the core coefficient remains stable under multiple algorithms and passes the 1% significance test. This indicates that the policy can empower digital transformation through two paths: reducing debt financing costs and enhancing green innovation output, thereby providing empirical evidence for the coordinated promotion of the “dual carbon” strategy and the development of the digital economy.

 

Keywords
Financing constraints; Carbon emissions trading; Dual machine learning; Porter hypothesis; Digital transformation; green innovation

Related Articles

Liqin Zheng1, Dongrui Qing2, Yan Zhang1
1School of Mathematics and Statistics, Shaan Xi Xue Qian Normal University Xi’an 710100, P.R.China
2School of Marxism, Xi’an University of Finance and Economics Xi’an 710100, P.R.China
Yanan Gao1, Aiqun Peng2, Nina Ma2
1Management School of Anhui Business and Technology College Hefei 230000, Anhui, China
2Economics and Trade School of Anhui Business and Technology College Hefei 230000, Anhui, China
Ya’ning Liu1, Ping Ma1
1School of Teacher Education, Shihezi University, Shihezi, Xinjiang, 832000, China
Yuhui Li1, Zhongliang Gong1
1College of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China
Hanqing Hu1, Chengjin Liu1, Tianmu Tian1
1School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 100192